3 citations · 4 across the 6 of their papers we have counts for
8 papers
Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning
Yuzhou Wei, Wenjie Liu, Yifan Xie +3
Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing da…
A Koopman Set-Membership Approach for Nonlinear Data-Driven Control with Stability Guarantees
Yifan Xie, Zuxun Xiong, Julian Berberich +2
This paper proposes a data-driven controller design method for unknown nonlinear systems based on a Koopman bilinear realization. Using Koopman operator theory, the nonlinear syste…
Data-Driven Min-Max MPC with Integral Quadratic Constraints
Yifan Xie, Julian Berberich, Frank Allgöwer
Data-driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling…
Adaptive Data-Driven Min-Max MPC for Linear Time-Varying Systems
Yifan Xie, Julian Berberich, Frank Allgöwer
In this paper, we propose an adaptive data-driven min-max model predictive control (MPC) scheme for discrete-time linear time-varying (LTV) systems. We assume that prior knowledge…
Bilinear Data-Driven Min-Max MPC: Designing Rational Controllers via Sum-of-squares Optimization
Yifan Xie, Julian Berberich, Robin Strässer +1
We propose a data-driven min-max model predictive control (MPC) scheme to control unknown discrete-time bilinear systems. Based on a sequence of noisy input-state data, we state a…
Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal
Yifan Xie, Julian Berberich, Felix Brändle +1
This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches,…
Data-Driven Min-Max MPC for Linear Systems: Robustness and Adaptation
Yifan Xie, Julian Berberich, Frank Allgöwer
Data-driven controllers design is an important research problem, in particular when data is corrupted by the noise. In this paper, we propose a data-driven min-max model predictive…
Data-Driven Min-Max MPC for Linear Systems
Yifan Xie, Julian Berberich, Frank Allgower
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are…